Top Football Leagues Scorers

Dataset statistics

Number of variables	15
Number of observations	660
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	77.5 KiB
Average record size in memory	120.2 B

Variable types

Categorical	5
Numeric	10

Alerts

`Club` has a high cardinality: 180 distinct values	High cardinality
`Player Names` has a high cardinality: 444 distinct values	High cardinality
`Matches_Played` is highly correlated with `Mins` and 5 other fields	High correlation
`Mins` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`Goals` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`xG` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`xG Per Avg Match` is highly correlated with `Shots Per Avg Match` and 1 other fields	High correlation
`Shots` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`OnTarget` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`Shots Per Avg Match` is highly correlated with `xG Per Avg Match` and 1 other fields	High correlation
`On Target Per Avg Match` is highly correlated with `xG Per Avg Match` and 1 other fields	High correlation
`Year` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`Matches_Played` is highly correlated with `Mins` and 5 other fields	High correlation
`Mins` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`Goals` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`xG` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`xG Per Avg Match` is highly correlated with `Shots Per Avg Match` and 1 other fields	High correlation
`Shots` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`OnTarget` is highly correlated with `Matches_Played` and 6 other fields	High correlation
`Shots Per Avg Match` is highly correlated with `xG Per Avg Match` and 2 other fields	High correlation
`On Target Per Avg Match` is highly correlated with `xG Per Avg Match` and 2 other fields	High correlation
`Year` is highly correlated with `Matches_Played` and 2 other fields	High correlation
`Matches_Played` is highly correlated with `Mins` and 4 other fields	High correlation
`Mins` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`Goals` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`xG` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`Shots` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`OnTarget` is highly correlated with `Matches_Played` and 4 other fields	High correlation
`Shots Per Avg Match` is highly correlated with `On Target Per Avg Match`	High correlation
`On Target Per Avg Match` is highly correlated with `Shots Per Avg Match`	High correlation
`League` is highly correlated with `Country`	High correlation
`Country` is highly correlated with `League`	High correlation
`Country` is highly correlated with `League`	High correlation
`League` is highly correlated with `Country`	High correlation
`Matches_Played` is highly correlated with `Mins` and 5 other fields	High correlation
`Mins` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`Goals` is highly correlated with `Matches_Played` and 8 other fields	High correlation
`xG` is highly correlated with `Matches_Played` and 8 other fields	High correlation
`xG Per Avg Match` is highly correlated with `Goals` and 4 other fields	High correlation
`Shots` is highly correlated with `Matches_Played` and 8 other fields	High correlation
`OnTarget` is highly correlated with `Matches_Played` and 7 other fields	High correlation
`Shots Per Avg Match` is highly correlated with `Goals` and 5 other fields	High correlation
`On Target Per Avg Match` is highly correlated with `Goals` and 5 other fields	High correlation
`Year` is highly correlated with `Matches_Played` and 5 other fields	High correlation
`Player Names` is uniformly distributed	Uniform
`Substitution` has 169 (25.6%) zeros	Zeros

Reproduction

Analysis started	2021-11-26 17:43:14.882867
Analysis finished	2021-11-26 17:43:40.299916
Duration	25.42 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

Country
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	9
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

Spain	100
Italy	100
Germany	100
Brazil	100
England	80
Other values (4)	180

Length

Max length	12
Median length	6
Mean length	6.333333333
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Spain
2nd row	Spain
3rd row	Spain
4th row	Spain
5th row	Spain

Common Values

Value	Count	Frequency (%)
Spain	100	15.2%
Italy	100	15.2%
Germany	100	15.2%
Brazil	100	15.2%
England	80	12.1%
France	60	9.1%
USA	40	6.1%
Portugal	40	6.1%
Netherlands	40	6.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
spain	100	15.2%
italy	100	15.2%
germany	100	15.2%
brazil	100	15.2%
england	80	12.1%
france	60	9.1%
usa	40	6.1%
portugal	40	6.1%
netherlands	40	6.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

League
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	28
Distinct (%)	4.2%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

La Liga	100
Serie A	100
Bundesliga	100
Campeonato Brasileiro SÃ©rie A	100
Premier League	80
Other values (23)	180

Length

Max length	30
Median length	10
Mean length	12.77727273
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	La Liga
2nd row	La Liga
3rd row	La Liga
4th row	La Liga
5th row	La Liga

Common Values

Value	Count	Frequency (%)
La Liga	100	15.2%
Serie A	100	15.2%
Bundesliga	100	15.2%
Campeonato Brasileiro SÃ©rie A	100	15.2%
Premier League	80	12.1%
Primeira Liga	40	6.1%
MLS	40	6.1%
Eredivisie	40	6.1%
France Ligue 12	3	0.5%
France Ligue 9	3	0.5%
Other values (18)	54	8.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
a	200	14.3%
liga	140	10.0%
la	100	7.1%
serie	100	7.1%
bundesliga	100	7.1%
campeonato	100	7.1%
brasileiro	100	7.1%
sã©rie	100	7.1%
premier	80	5.7%
league	80	5.7%
Other values (25)	300	21.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Club
Categorical

HIGH CARDINALITY

Distinct	180
Distinct (%)	27.3%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

None	34
(PSG)	14
(BAR)	13
(NAP)	13
(RMA)	11
Other values (175)	575

Length

Max length	29
Median length	5
Mean length	4.998484848
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	64 ?
Unique (%)	9.7%

Sample

1st row	(BET)
2nd row	(BAR)
3rd row	(ATL)
4th row	(CAR)
5th row	(VAL)

Common Values

Value	Count	Frequency (%)
None	34	5.2%
(PSG)	14	2.1%
(BAR)	13	2.0%
(NAP)	13	2.0%
(RMA)	11	1.7%
(SOC)	11	1.7%
(ATA)	11	1.7%
(FLA)	11	1.7%
(TOT)	11	1.7%
(INT)	10	1.5%
Other values (170)	521	78.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
none	34	5.1%
psg	14	2.1%
bar	13	2.0%
nap	13	2.0%
rma	11	1.7%
soc	11	1.7%
ata	11	1.7%
fla	11	1.7%
tot	11	1.7%
liv	10	1.5%
Other values (174)	526	79.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Player Names
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	444
Distinct (%)	67.3%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

Andrea Belotti	5
Lionel Messi	5
Luis Suarez	5
Andrej Kramaric	5
Ciro Immobile	5
Other values (439)	635

Length

Max length	25
Median length	13
Mean length	12.76212121
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	317 ?
Unique (%)	48.0%

Sample

1st row	Juanmi Callejon
2nd row	Antoine Griezmann
3rd row	Luis Suarez
4th row	Ruben Castro
5th row	Kevin Gameiro

Common Values

Value	Count	Frequency (%)
Andrea Belotti	5	0.8%
Lionel Messi	5	0.8%
Luis Suarez	5	0.8%
Andrej Kramaric	5	0.8%
Ciro Immobile	5	0.8%
Cristiano Ronaldo	5	0.8%
Robert Lewandowski	5	0.8%
Timo Werner	5	0.8%
Iago Aspas	5	0.8%
Fabio Quagliarella	5	0.8%
Other values (434)	610	92.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
diego	9	0.7%
bruno	9	0.7%
kevin	8	0.6%
luis	8	0.6%
carlos	8	0.6%
raul	7	0.6%
fabio	7	0.6%
iago	7	0.6%
andrea	7	0.6%
de	7	0.6%
Other values (698)	1173	93.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Matches_Played
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	37
Distinct (%)	5.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	22.37121212

Minimum	2
Maximum	38
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	2
5-th percentile	6
Q1	14
median	24
Q3	31
95-th percentile	36
Maximum	38
Range	36
Interquartile range (IQR)	17

Descriptive statistics

Standard deviation	9.754657502
Coefficient of variation (CV)	0.4360361633
Kurtosis	-1.130325488
Mean	22.37121212
Median Absolute Deviation (MAD)	7
Skewness	-0.3692394082
Sum	14765
Variance	95.15334299
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=37)

Value	Count	Frequency (%)
31	32	4.8%
9	32	4.8%
32	30	4.5%
29	30	4.5%
23	30	4.5%
33	29	4.4%
26	28	4.2%
10	26	3.9%
24	26	3.9%
27	25	3.8%
Other values (27)	372	56.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	1	0.2%
3	5	0.8%
4	3	0.5%
5	6	0.9%
6	24	3.6%
7	22	3.3%
8	24	3.6%
9	32	4.8%
10	26	3.9%
11	12	1.8%

Value	Count	Frequency (%)
38	4	0.6%
37	13	2.0%
36	17	2.6%
35	24	3.6%
34	21	3.2%
33	29	4.4%
32	30	4.5%
31	32	4.8%
30	24	3.6%
29	30	4.5%

Substitution
Real number (ℝ_≥0)

ZEROS

Distinct	21
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.224242424

Minimum	0
Maximum	26
Zeros	169
Zeros (%)	25.6%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	2
Q3	5
95-th percentile	11
Maximum	26
Range	26
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.839498442
Coefficient of variation (CV)	1.190821885
Kurtosis	4.896312268
Mean	3.224242424
Median Absolute Deviation (MAD)	2
Skewness	1.922155203
Sum	2128
Variance	14.74174829
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
0	169	25.6%
1	130	19.7%
2	80	12.1%
3	65	9.8%
5	43	6.5%
4	35	5.3%
6	34	5.2%
7	21	3.2%
8	20	3.0%
10	13	2.0%
Other values (11)	50	7.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	169	25.6%
1	130	19.7%
2	80	12.1%
3	65	9.8%
4	35	5.3%
5	43	6.5%
6	34	5.2%
7	21	3.2%
8	20	3.0%
9	10	1.5%

Value	Count	Frequency (%)
26	1	0.2%
23	2	0.3%
18	3	0.5%
17	1	0.2%
16	1	0.2%
15	5	0.8%
14	2	0.3%
13	5	0.8%
12	10	1.5%
11	10	1.5%

Mins
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	583
Distinct (%)	88.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2071.416667

Minimum	264
Maximum	4177
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	264
5-th percentile	574.95
Q1	1363.5
median	2245.5
Q3	2822
95-th percentile	3271.25
Maximum	4177
Range	3913
Interquartile range (IQR)	1458.5

Descriptive statistics

Standard deviation	900.595049
Coefficient of variation (CV)	0.4347725223
Kurtosis	-1.052411379
Mean	2071.416667
Median Absolute Deviation (MAD)	659
Skewness	-0.3573606633
Sum	1367135
Variance	811071.4422
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3115	3	0.5%
2312	3	0.5%
872	3	0.5%
669	3	0.5%
567	3	0.5%
822	3	0.5%
903	3	0.5%
1786	2	0.3%
2822	2	0.3%
3236	2	0.3%
Other values (573)	633	95.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
264	1	0.2%
280	1	0.2%
293	1	0.2%
337	1	0.2%
356	1	0.2%
387	1	0.2%
396	1	0.2%
397	1	0.2%
451	1	0.2%
452	1	0.2%

Value	Count	Frequency (%)
4177	1	0.2%
3931	1	0.2%
3651	1	0.2%
3641	1	0.2%
3555	1	0.2%
3533	2	0.3%
3511	1	0.2%
3491	1	0.2%
3474	1	0.2%
3448	1	0.2%

Goals
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	33
Distinct (%)	5.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	11.78484848

Minimum	2
Maximum	37
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	2
5-th percentile	4
Q1	8
median	11
Q3	14
95-th percentile	23
Maximum	37
Range	35
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	5.98245392
Coefficient of variation (CV)	0.507639443
Kurtosis	2.330457684
Mean	11.78484848
Median Absolute Deviation (MAD)	3
Skewness	1.181334957
Sum	7778
Variance	35.78975491
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
11	67	10.2%
12	64	9.7%
10	55	8.3%
9	50	7.6%
4	48	7.3%
13	47	7.1%
14	37	5.6%
8	36	5.5%
15	30	4.5%
16	29	4.4%
Other values (23)	197	29.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	9	1.4%
3	14	2.1%
4	48	7.3%
5	28	4.2%
6	20	3.0%
7	25	3.8%
8	36	5.5%
9	50	7.6%
10	55	8.3%
11	67	10.2%

Value	Count	Frequency (%)
37	1	0.2%
36	3	0.5%
34	1	0.2%
33	2	0.3%
31	3	0.5%
30	1	0.2%
29	5	0.8%
28	4	0.6%
26	3	0.5%
25	6	0.9%

xG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	558
Distinct (%)	84.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.08960606

Minimum	0.71
Maximum	32.54
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	0.71
5-th percentile	2.2775
Q1	6.1
median	9.285
Q3	13.2525
95-th percentile	20.5105
Maximum	32.54
Range	31.83
Interquartile range (IQR)	7.1525

Descriptive statistics

Standard deviation	5.72484367
Coefficient of variation (CV)	0.5674001181
Kurtosis	1.265816144
Mean	10.08960606
Median Absolute Deviation (MAD)	3.45
Skewness	0.9563577449
Sum	6659.14
Variance	32.77383505
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
11.62	4	0.6%
3.33	4	0.6%
6.19	3	0.5%
6.11	3	0.5%
11.12	3	0.5%
14.51	3	0.5%
8.56	3	0.5%
8.7	3	0.5%
9.39	3	0.5%
11.09	3	0.5%
Other values (548)	628	95.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.71	1	0.2%
0.8	1	0.2%
0.96	1	0.2%
1.03	1	0.2%
1.05	1	0.2%
1.12	1	0.2%
1.13	1	0.2%
1.31	1	0.2%
1.39	1	0.2%
1.43	1	0.2%

Value	Count	Frequency (%)
32.54	1	0.2%
31.17	1	0.2%
31.05	1	0.2%
30.6	1	0.2%
30.52	1	0.2%
29.27	1	0.2%
29	1	0.2%
28.94	1	0.2%
27.32	1	0.2%
26.65	1	0.2%

xG Per Avg Match
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	92
Distinct (%)	13.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.4761666667

Minimum	0.07
Maximum	1.35
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	0.07
5-th percentile	0.24
Q1	0.34
median	0.435
Q3	0.57
95-th percentile	0.8605
Maximum	1.35
Range	1.28
Interquartile range (IQR)	0.23

Descriptive statistics

Standard deviation	0.1928313189
Coefficient of variation (CV)	0.404966018
Kurtosis	1.912739847
Mean	0.4761666667
Median Absolute Deviation (MAD)	0.105
Skewness	1.176635667
Sum	314.27
Variance	0.03718391755
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.33	23	3.5%
0.39	21	3.2%
0.37	21	3.2%
0.4	20	3.0%
0.41	20	3.0%
0.43	19	2.9%
0.34	18	2.7%
0.57	18	2.7%
0.31	16	2.4%
0.45	16	2.4%
Other values (82)	468	70.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.07	1	0.2%
0.09	1	0.2%
0.15	2	0.3%
0.16	5	0.8%
0.17	1	0.2%
0.18	4	0.6%
0.19	2	0.3%
0.2	1	0.2%
0.21	4	0.6%
0.22	4	0.6%

Value	Count	Frequency (%)
1.35	1	0.2%
1.27	1	0.2%
1.19	2	0.3%
1.16	1	0.2%
1.12	1	0.2%
1.1	1	0.2%
1.08	1	0.2%
1.06	4	0.6%
1.05	1	0.2%
1.01	1	0.2%

Shots
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	144
Distinct (%)	21.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	64.17727273

Minimum	5
Maximum	208
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	5
5-th percentile	14
Q1	37.75
median	62
Q3	86
95-th percentile	124.05
Maximum	208
Range	203
Interquartile range (IQR)	48.25

Descriptive statistics

Standard deviation	34.94162179
Coefficient of variation (CV)	0.5444547627
Kurtosis	0.6264647759
Mean	64.17727273
Median Absolute Deviation (MAD)	24
Skewness	0.6761502446
Sum	42357
Variance	1220.916933
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
65	13	2.0%
21	13	2.0%
18	12	1.8%
55	12	1.8%
52	11	1.7%
81	11	1.7%
87	10	1.5%
56	10	1.5%
80	9	1.4%
54	9	1.4%
Other values (134)	550	83.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
5	2	0.3%
6	2	0.3%
7	2	0.3%
8	1	0.2%
9	2	0.3%
10	3	0.5%
11	3	0.5%
12	5	0.8%
13	7	1.1%
14	7	1.1%

Value	Count	Frequency (%)
208	1	0.2%
197	1	0.2%
179	1	0.2%
178	2	0.3%
177	1	0.2%
170	1	0.2%
167	1	0.2%
162	1	0.2%
159	2	0.3%
152	1	0.2%

OnTarget
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	79
Distinct (%)	12.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	28.36515152

Minimum	2
Maximum	102
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	2
5-th percentile	7
Q1	17
median	26
Q3	37
95-th percentile	58
Maximum	102
Range	100
Interquartile range (IQR)	20

Descriptive statistics

Standard deviation	16.36314925
Coefficient of variation (CV)	0.5768750869
Kurtosis	2.36577599
Mean	28.36515152
Median Absolute Deviation (MAD)	10
Skewness	1.156283867
Sum	18721
Variance	267.7526532
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25	26	3.9%
24	20	3.0%
23	20	3.0%
33	20	3.0%
19	20	3.0%
30	19	2.9%
8	19	2.9%
28	18	2.7%
17	17	2.6%
37	17	2.6%
Other values (69)	464	70.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	3	0.5%
3	4	0.6%
4	3	0.5%
5	12	1.8%
6	6	0.9%
7	17	2.6%
8	19	2.9%
9	14	2.1%
10	10	1.5%
11	15	2.3%

Value	Count	Frequency (%)
102	1	0.2%
99	1	0.2%
98	1	0.2%
95	1	0.2%
91	1	0.2%
87	1	0.2%
86	1	0.2%
81	1	0.2%
79	1	0.2%
78	2	0.3%

Shots Per Avg Match
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	280
Distinct (%)	42.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.948015152

Minimum	0.8
Maximum	7.2
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	0.8
5-th percentile	1.709
Q1	2.335
median	2.845
Q3	3.3825
95-th percentile	4.54
Maximum	7.2
Range	6.4
Interquartile range (IQR)	1.0475

Descriptive statistics

Standard deviation	0.9149064812
Coefficient of variation (CV)	0.3103466007
Kurtosis	1.985334359
Mean	2.948015152
Median Absolute Deviation (MAD)	0.53
Skewness	0.9473020506
Sum	1945.69
Variance	0.8370538693
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2.47	8	1.2%
2.86	8	1.2%
2.75	7	1.1%
2.6	7	1.1%
3.05	7	1.1%
2.22	7	1.1%
2.65	6	0.9%
3.02	6	0.9%
2.49	6	0.9%
2.26	6	0.9%
Other values (270)	592	89.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.8	1	0.2%
0.81	1	0.2%
0.82	1	0.2%
0.85	1	0.2%
0.99	1	0.2%
1.03	1	0.2%
1.16	1	0.2%
1.2	2	0.3%
1.24	1	0.2%
1.27	1	0.2%

Value	Count	Frequency (%)
7.2	1	0.2%
7.12	1	0.2%
6.32	1	0.2%
6.22	1	0.2%
5.99	1	0.2%
5.97	1	0.2%
5.89	1	0.2%
5.84	2	0.3%
5.67	1	0.2%
5.59	1	0.2%

On Target Per Avg Match
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	184
Distinct (%)	27.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.315651515

Minimum	0.24
Maximum	3.63
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.3 KiB

Quantile statistics

Minimum	0.24
5-th percentile	0.72
Q1	0.98
median	1.25
Q3	1.54
95-th percentile	2.241
Maximum	3.63
Range	3.39
Interquartile range (IQR)	0.56

Descriptive statistics

Standard deviation	0.4742392996
Coefficient of variation (CV)	0.3604596613
Kurtosis	2.239211943
Mean	1.315651515
Median Absolute Deviation (MAD)	0.28
Skewness	1.180931921
Sum	868.33
Variance	0.2249029133
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.25	13	2.0%
0.94	12	1.8%
1.28	12	1.8%
1.21	12	1.8%
0.96	11	1.7%
1.3	10	1.5%
1.29	10	1.5%
1	10	1.5%
1.11	9	1.4%
1.26	9	1.4%
Other values (174)	552	83.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.24	1	0.2%
0.29	1	0.2%
0.4	1	0.2%
0.48	2	0.3%
0.5	1	0.2%
0.52	1	0.2%
0.54	1	0.2%
0.55	2	0.3%
0.56	4	0.6%
0.58	1	0.2%

Value	Count	Frequency (%)
3.63	1	0.2%
3.11	2	0.3%
3.04	1	0.2%
2.94	1	0.2%
2.9	1	0.2%
2.89	2	0.3%
2.85	1	0.2%
2.84	1	0.2%
2.83	1	0.2%
2.77	1	0.2%

Year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	5.3 KiB

2019	200
2020	160
2018	120
2016	100
2017	80

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2016
2nd row	2016
3rd row	2016
4th row	2016
5th row	2016

Common Values

Value	Count	Frequency (%)
2019	200	30.3%
2020	160	24.2%
2018	120	18.2%
2016	100	15.2%
2017	80	12.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2019	200	30.3%
2020	160	24.2%
2018	120	18.2%
2016	100	15.2%
2017	80	12.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	Country	League	Club	Player Names	Matches_Played	Substitution	Mins	Goals	xG	xG Per Avg Match	Shots	OnTarget	Shots Per Avg Match	On Target Per Avg Match	Year
0	Spain	La Liga	(BET)	Juanmi Callejon	19	16	1849	11	6.62	0.34	48	20	2.47	1.03	2016
1	Spain	La Liga	(BAR)	Antoine Griezmann	36	0	3129	16	11.86	0.36	88	41	2.67	1.24	2016
2	Spain	La Liga	(ATL)	Luis Suarez	34	1	2940	28	23.21	0.75	120	57	3.88	1.84	2016
3	Spain	La Liga	(CAR)	Ruben Castro	32	3	2842	13	14.06	0.47	117	42	3.91	1.40	2016
4	Spain	La Liga	(VAL)	Kevin Gameiro	21	10	1745	13	10.65	0.58	50	23	2.72	1.25	2016
5	Spain	La Liga	(JUV)	Cristiano Ronaldo	29	0	2634	25	24.68	0.89	162	60	5.84	2.16	2016
6	Spain	La Liga	(RMA)	Karim Benzema	23	6	1967	11	13.25	0.64	69	34	3.33	1.64	2016
7	Spain	La Liga	(PSG)	Neymar	30	0	2694	13	13.33	0.47	105	42	3.70	1.48	2016
8	Spain	La Liga	(CEL)	Iago Aspas	25	7	2354	19	13.88	0.56	78	37	3.15	1.49	2016
9	Spain	La Liga	(EIB)	Sergi Enrich	31	7	2904	11	8.25	0.27	64	26	2.09	0.85	2016

Last rows

	Country	League	Club	Player Names	Matches_Played	Substitution	Mins	Goals	xG	xG Per Avg Match	Shots	OnTarget	Shots Per Avg Match	On Target Per Avg Match	Year
650	Netherlands	Eredivisie	(AJA)	Klaas-Jan Huntelaar	6	12	938	9	6.91	0.70	32	17	3.24	1.72	2020
651	Netherlands	Eredivisie	(WIL)	Vangelis Pavlidis	25	0	2265	11	12.64	0.53	70	31	2.94	1.30	2020
652	Netherlands	Eredivisie	(EMM)	Michael de Leeuw	26	0	2383	9	8.28	0.33	51	23	2.03	0.92	2020
653	Netherlands	Eredivisie	(PSV)	Donyell Malen	14	0	1245	11	8.91	0.68	59	32	4.50	2.44	2020
654	Netherlands	Eredivisie	(RZA)	Haris Vuckic	23	2	2194	11	6.00	0.26	38	17	1.65	0.74	2020
655	Netherlands	Eredivisie	(UTR)	Gyrano Kerk	24	0	2155	10	7.49	0.33	50	18	2.20	0.79	2020
656	Netherlands	Eredivisie	(AJA)	Quincy Promes	18	2	1573	12	9.77	0.59	56	30	3.38	1.81	2020
657	Netherlands	Eredivisie	(PSV)	Denzel Dumfries	25	0	2363	7	5.72	0.23	45	14	1.81	0.56	2020
658	Netherlands	Eredivisie	None	Cyriel Dessers	26	0	2461	15	14.51	0.56	84	43	3.24	1.66	2020
659	Netherlands	Eredivisie	(PSV)	Cody Gakpo	14	11	1557	7	4.43	0.27	38	15	2.32	0.92	2020

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows